Electronics Forum | Tue Mar 16 17:34:27 EDT 2021 | dan_ems
Hello, can you help please with a question regarding Fuji NXT programming? During the placement of the components on the fuji interface the image of the components during alignement can be seen on the Fuji operator interface. My question is can be al
Electronics Forum | Wed Jun 24 08:28:16 EDT 2020 | pickplace
Hi Jacobo, it would be importatn to know what kind of error the machine is displaying. Are the chips in paper or plasic tape? Did you check pickup coordinates and hight? Have you many false edge pick vision errors? Then I would switch off the tombest
Used SMT Equipment | Pick and Place/Feeders
FUJI VPD Plus IBE SMT Inventory ID : 110516-016 FUJI Serial Number : 0035 Vintage : 2008 Details •Model: MPA 4000 •Includes Dongle and Dell PC Click Here for more information or to request a quotation... Specifications ( See Details abov
Industry News | 2020-06-30 15:07:32.0
MIRTEC is excited to announce its participation in an exclusive ONLINE CONFERENCE & EXPO. The event will be broadcast live on Wednesday, July 8, 2020 from 10 a.m. - 2:30 p.m. EST and will include a full program of live presentations, interviews and special guests.
Industry News | 2020-07-07 13:52:57.0
SEHO Systems GmbH, is the right partner when innovative ideas are required. With the PowerVision, SEHO focuses on two key points in the manufacturing of THT assemblies: Continuous quality assurance and cost-efficient production processes.
Parts & Supplies | Pick and Place/Feeders
GIC-F01 FUJI NXT Feeder Calibration JIG It is essential for this equipment that enhanced productivity , installation and quality improvement As the high efficiency vision camera is asked in order to investigate precisely and repair the error of
Parts & Supplies | Pick and Place/Feeders
GIC-F01 FUJI NXT Feeder Calibration JIG It is essential for this equipment that enhanced productivity , installation and quality improvement As the high efficiency vision camera is asked in order to investigate precisely and repair the error of
Technical Library | 2023-11-20 18:10:20.0
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.
SMTnet Express, April 16, 2015, Subscribers: 22,628, Members: Companies: 14,302, Users: 38,055 Stereo Vision Based Automated Solder Ball Height Detection Jinjin Li, Lina J. Karam; School of Electrical, Computer, & Energy Engineering, Arizona State
.png)
.jpg)